AI in Finance Summit New York schedule

•    How AI and ML are revolutionizing traditional financial products and services, such as lending, insurance, and wealth management?
•    Discovering the potential of AI and ML to create new revenue streams and improve profitability for financial institutions
•    The future directions of AI and ML in financial products, emerging technologies and potential applications

 

 

Motivated by the persistent pressure points in counter FinCrime initiatives, the session will address the methodologies for synthetic dataset generation and their efficacy in fraud and money laundering use cases, as an area of growing interest. Also boosted by new Generative AI capabilities, the option for tailoring datasets to desired use cases for model training, opens new avenues for enhanced coverage of possible fraud landscapes. What is more, simulated datasets promise to overcome the ‘Collective Intelligence conundrum’, that is, the inability to share transaction data within networks on account of Personally Identifiable Information (PII) and privacy.  The questions being addressed now become more acute on (I) how useful synthetic data can be to train models, as measured for (a) ML-efficacy; (b) privacy and (c) fidelity, and (II) what the limits between generative capability and true data representativeness are - and how far we can and should go with simulated data.  

•    How customizing LLMs can significantly enhance their performance and relevance to specific financial tasks?
•    Explaining the key benefits of customization, such as improved accuracy, efficiency, and decision-making
•    Real-world applications and success stories

 

 

•    What is the next chapter in the usage of Data in AI?    
•    What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
•    How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency in the financial services industry?

•    Diving into the challenges of creating a regulatory framework for AI that is both flexible and effective
•    How financial institutions can build trust with customers by providing clear explanations of how AI decisions are made?
•    Identifying the ethical principles that should guide the development and deployment of AI in finance, such as fairness, accountability, and transparency

 

•    What are the key challenges financial institutions face when transitioning from AI hype to practical implementation, and how can they overcome them?
•    How can banks ensure that AI solutions are aligned with both regulatory requirements and business goals to deliver real value?
•    What role does data quality and governance play in the successful adoption of AI in financial services, and what steps should organizations take to improve it?
•    How can financial institutions manage the risks associated with AI, such as biases in algorithms and cybersecurity threats, while still maximizing the technology’s potential?

•    What are the key challenges banks face in keeping up with the rapidly evolving landscape of AI regulations?
•    How can Large Language Models (LLMs) be leveraged to streamline the process of comparing and analyzing AI regulations?
•    What are the specific benefits of using LLMs for regulatory compliance, particularly in terms of efficiency, accuracy, and insights gained?

•    Building a strong foundation for AI: The importance of ensuring data is accurate, complete, and consistent to train reliable AI Models
•    Defining the critical role of data governance in protecting sensitive customer information
•    Outlining the core principles that should guide your organization's data governance efforts
•    Identifying the role of data governance tools and platforms in automating processes and improving data quality

•    What are the most significant opportunities that AI presents for insurance companies today?
•    How can insurance companies effectively integrate AI into their existing systems and processes?
•    What role does AI play in personalizing insurance products and services for individual customers?
•    What ethical considerations should insurers keep in mind when deploying AI technologies?

•    How can we ensure that the development and application of generative AI in quantitative finance aligns with ethical principles and avoids biases?
•    What are the specific data requirements for training and fine-tuning generative AI models in the context of quantitative finance? How can we address data quality and quantity challenges?
•    Given the complexity of generative AI models, how can we enhance their interpretability and explainability to build trust and facilitate regulatory compliance?

•    Misusing GenAI diminishes the value of AI in organizations
•    Determining whether GenAI makes sense for your use case
•    Considering alternative AI techniques
•    Combining GenAI models with AI other techniques

 

 

•    Evolution of Generative AI in the Workplace - From Personalization to Impersonation: Exploring the shift from AI enhancing personalized experiences to AI performing tasks that mimic human behavior, with cross-industry examples showcasing AI's diverse applications. •    Human-AI Collaboration - Enhancing Capabilities and Ethical Considerations: How AI complements human work to increase productivity and efficiency, while addressing ethical concerns about AI impersonation and maintaining human interaction. •    Future of Work- Adapting to and Predicting Change: Strategies for adapting to the evolving AI landscape and potential future scenarios, from utopian collaboration to dystopian job loss

•    Identifying how LLMs are transforming the analysis of unstructured financial data
•    How LLMs are enabling the development of innovative fintech product
•    Reviewing the current NLP trends and latest SOTA algorithms Overview of the latest NLP algorithms and industry use cases that are easier to solve using the open-source NLP methods

• •    Proving the real value of initiatives as fast as the business expect 
  •    Is there a right balance between the emphasis on AI tools, technologies and models, vs ensuring teams spend enough time on measuring incremental value of AI projects? 
  •    Balancing short-term wins with long-term success 
  •    Key steps towards addressing and improving return on investments

•    How AI/ML is revolutionizing customer interactions in financial services through personalized recommendations, AI-driven chatbots, and predictive analytics, creating more engaging and efficient customer experiences?
•    Exploring how AI/ML technologies are improving the accuracy and speed of risk assessments and fraud detection in real-time, helping financial institutions protect assets and maintain trust while reducing operational costs
•    How AI/ML is enabling the creation of new financial products and services

•    How can we ensure that AI models used in banking are fair, unbiased, and ethically sound?
•    How can we make AI models more transparent and explainable to both internal stakeholders and customers?
•    What are the best practices for communicating the decision-making process of AI systems?
•    How can banks navigate the evolving regulatory landscape for AI and machine learning

•    Customer-centric digital banking solutions require organized and trusted data that is business-ready for analytics and AI model building 
•    Sourcing, identifying, and prioritizing high-value AI projects throughout the organization 
•    Building a team at scale: acquisition and development of world-class talent  
•    Ensuring topics such as fairness, explainability & ethics are properly addressed 

•    How open banking is reshaping data access and analytics by providing a more comprehensive view of financial data?
•    How can AI improve the customer experience within open banking? 
•    What can we expect to see from FinTech in the near future and what impacts will this have?